Overview

Credit scoring estimates the likelihood that people or firms will repay loans, guiding approvals, pricing, limits, and monitoring. Work ranges from logistic regression and boosted trees to neural and graph models, with newer approaches using transaction histories, alternative data, tabular foundation models, and federated learning. Researchers evaluate these systems on datasets such as LendingClub, Home Credit, mortgage records, and small-business portfolios, measuring default ranking alongside calibration, recall, robustness to shifts, interpretability, and group fairness. Predictive gains do not guarantee fair or useful decisions: proxy variables, selective labels, changing economic conditions, and explanation quality remain difficult, while privacy and governance constrain which data and adaptations can be used.

Latest papers 39

9 papers in the last four weeks, up 13% on the four weeks before. 0.1% of all new papers.

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CardsList
  1. Learned Monotone Recurrent Features in Governed Credit Scoring: The Price of the Frame and the Necessity of Macro Conditioning

    Oct 6, 2026Yew Lee TanCredit ScoringRecurrent Neural Networks

  2. PaMIR: Open Benchmark of Public Credit-Default Datasets

    Oct 2, 2026Mikhail Liashkov, Ilyas Varshavskiy, Shuhratjon Khalilbekov +2Credit ScoringTabular Prediction

  3. CredWise: A Controlled Agentic Decision-Intelligence Framework for Explainable and Auditable Credit-Risk Assessment

    Sep 29, 2026Aakash Kumar TiwariAI-Assisted Decision MakingCredit Scoring

  4. FedIncome: Federated Learning for Income Estimation in Digital Lending Under Data Sovereignty Constraints

    Sep 23, 2026Sultan Amed, Tanmay Sen, Sayantan BanerjeeCredit ScoringFinance

  5. DefaultGNN: A Dual-Perspective GNN Framework for Predicting Corporate Default from Buyer-Seller Transaction Networks

    Sep 22, 2026Junghoon Kim, Hyunsung Kim, Seungyoon Choi +4Graph Neural NetworksGraph Representation Learning

  6. A Decision-Support Audit Protocol for Supervision Drift in Proxy-Labeled Credit-Risk Prediction

    Sep 14, 2026Mehrdad Shoeibi, Muhammad Shabanpour, Waldemar Karwowski +1AI Risk ManagementAlgorithmic Auditing

  7. Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data

    Sep 14, 2026LiYang Wang, Zhen Zhong, Zhen Tian +2AI Risk ManagementCredit Scoring

  8. Adversarial Training for Tabular Credit Scoring: A Multi-Attack Robustness Evaluation in P2P Lending

    Sep 9, 2026Gijs A. F. Niewzwaag, Marijn G. S. Veth, Manuele Massei +1Adversarial TrainingAdversarial Attacks

  9. When Does a Classifier Help an LLM? Classifier-Guided Prompting and Hybrid Classifier-LLM Models for Credit-Default Prediction

    Aug 30, 2026Rishi Datta, Lavanya PrahalladLLM PromptingCredit Scoring

  10. Oculi: A Conversational Agentic Platform for Automated Credit Risk Analysis

    Aug 28, 2026Vennise Ho, Kristian Diana, Sandy Mourad +3Data Analysis AgentsCredit Scoring

  11. findr\texttt{findr}: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions

    Aug 25, 2026Victor Medina-Olivares, Stefan Lessmann, Jonathan CrookInterpretable MLCredit Scoring

  12. FraudBench: Protocol-Sensitive Benchmarking of Adversarial Robustness for Financial Risk Assessment

    Aug 25, 2026Xitong Zeng, Zhaoge Bi, Yitian Yang +2Adversarial RobustnessAdversarial Attacks

  13. Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models

    Aug 18, 2026Sahab Zandi, Noah Kostesku, Christophe Mues +2Explainable Artificial IntelligenceFaithfulness of Language Model Explanations

  14. Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk

    Aug 8, 2026Gregorius Reynaldi Pratama, Kuo-Kun TsengFeature AttributionFaithfulness of Language Model Explanations

  15. Empowering Credit Risk Detection in Weixin Pay with Billion-Scale Deep Graph Learning

    Aug 3, 2026Xin Liu, Xiyuan Chen, Chenglong Wu +3AI Risk ManagementFinancial Fraud Detection

  16. The Illusion of Improvement: Reject Inference Strategies in Credit Scoring

    Jun 16, 2026Bruno Scarone, Ricardo Baeza-YatesCredit ScoringSelection Bias

  17. When Do Data-Driven Systems Exhibit the Capability to Infer?

    Jun 10, 2026Maximilian Poretschkin, Tabea NaevenAI GovernanceCredit Scoring

  18. Post-Quantum Secure Federated DeFi for Inclusive Banking

    Jun 9, 2026Swati Sachan, Dale Fickett, Richard Buchinger +1Financial ServicesDecentralized Finance

  19. Privacy-Preserving Credit Risk Prediction with Alternative Data

    Jun 9, 2026Hongzhe Zhang, Jiarong Xu, Jing He +1AI Risk ManagementPrivacy-Preserving ML

  20. Evolutionary Rule Extraction from Corporate Default Prediction Models

    May 28, 2026Desirè Fabbretti, Matteo Pasquino, Elia Pacioni +2Interpretable MLCredit Scoring

  21. Foundation Models for Credit Risk Prediction: A Game Changer?

    May 18, 2026Bart Baesens, Andreas Goethals, Stefan Lessmann +10Tabular Foundation ModelsAI Risk Management

  22. Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions

    May 12, 2026Gideon Popoola, John SheppardFeature AttributionCounterfactual Explanations

  23. Analog Optical Inference on Million-Record Mortgage Data

    Apr 14, 2026Sofia Berloff, Pavel Koptev, Konstantin MalkovPhotonic ComputingEfficient Neural Network Inference